TU‐C‐WAB‐01: Accuracy Requirements and Uncertainty Considerations in Radiation Therapy
Bibliographic record
Abstract
Recent years have seen major advances in the technology of radiation oncology allowing for a transition from 2‐D radiation therapy (RT) to 3‐D conformal RT, intensity modulated RT (IMRT), image‐guided RT (IGRT), adaptive RT (ART), and 4‐D imaging and motion management in RT. Brachytherapy procedures have evolved both for high dose rate (HDR) techniques as well as permanent implants, and image‐guided brachytherapy is the modern standard. While a number of publications have defined accuracy needs in radiation oncology, most of these reports were developed in an era with different radiation technologies and date back to the 1980s and 90s. In view of modern treatment procedures, improvements in dosimetry methodologies, and new clinical dose‐volume data, the AAPM 2011 summer school dealt with uncertainties in external beam radiation therapy and the International Atomic Energy Agency (IAEA) is completing a new guidance document on “Accuracy Requirements and Uncertainties in Radiation Therapy”. This symposium will review the latest information on accuracy requirements and uncertainty considerations in radiation therapy in terms of radiobiological rationale, clinical needs and a practical reality check. Learning Objectives: 1. To review historical and current data related to accuracy and uncertainties in the overall radiation treatment process. 2. To provide a radiobiological rationale for accuracy considerations in RT. 3. To provide a clinical rationale for accuracy considerations in RT. 4. To review recent data demonstrating realistically achievable accuracy levels in RT.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".